Abstract: Techniques for intelligently routing IO to a storage class memory (SCM) namespace are disclosed. A configuration for a namespace is determined, where the configuration indicates a type of IO that the namespace is structured to handle. Details about the configuration of the namespace are stored in a repository. A forwarding rule is generated based on the namespace's stored configuration. When incoming IO having attributes similar to that type is received, implementation of the forwarding rule causes the incoming IO to be directed to the namespace. Attributes of a particular incoming IO are determined. As a result of the attributes satisfying a similarity threshold relative to the type, the forwarding rule is implemented such that the particular incoming IO is directed to the namespace.

Abstract: Systems and methods are provided for context-aware maintenance window identification. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: estimate a completion time of a maintenance operation; predict future usage of the IHS; identify a time window for the maintenance operation based upon the estimation and the prediction.

Abstract: A message broker resource monitoring service obtains message broker resource parameter data of a resource, based on communications of a message broker. Based on the resource parameter data and historical data, the message broker resource monitoring service obtains a predicted message delivery time value, which can be in association with confidence. If the predicted message delivery time value satisfies a resource deletion criterion, e.g., the predicted message delivery time value, with sufficient confidence, exceeds a threshold value, the message broker resource monitoring service triggers an action to delete the resource. To obtain the predicted value, a regression such as symmetric conformal quantile regression can be applied to the parameter data, e.g., to obtain a predicted message delivery time/latency value.

Abstract: In general, in one aspect, the invention relates to a method for enabling enhanced logging. The method includes obtaining a log associated with a job; determining, using the log, that enhanced logging is to be enabled prior to initiating the job; enabling, in response to the determination, enhanced logging on at least one node, and initiating servicing of the job, after the enabling, on the at least one node.

Abstract: Embodiments described herein relate to a method for probabilistically forecasting the state of hardware components. The method may include obtaining data items corresponding to a hardware component and performing an analysis of the hardware component. The analysis may include making a variety of probability predictions as to whether a label from among a set of possible labels is likely to be the correct label. The set of probabilities from the aforementioned analysis are then analyzed to determine which predicted label has the tightest range, and the prediction with the tightest range for a certain label is displayed to a user in a ranked fashion that includes a quantity of such probability prediction ranges. Such a display may allow an administrator to take action as to which hardware components should be replaced and in what order.

Abstract: From among physical storage devices (PSDs) of a storage system, a set of two or more of the PSDs that are eligible for scrubbing may be determined; and from among the set, a relative eligibility of the PSDs may be determined. Conformance prediction analysis may be applied to determine the set and the relative eligibility of PSDs of the set. The conformance prediction analysis may determine a scrubbing eligibility classification (e.g., label), and a confidence value for the classification, which may serve as the relative eligibility of the PSD. The eligible PSDs may be ranked in an order according to determined confidence values, and may be further classified according to such order. The future workload of the storage system may be forecasted, and the scrubbing of PSDs may be scheduled based on the forecasted workload of the system and the relative eligibilities of the set of PSDs.

Abstract: Techniques for intelligently routing IO to a storage class memory (SCM) namespace are disclosed. A configuration for a namespace is determined, where the configuration indicates a type of IO that the namespace is structured to handle. Details about the configuration of the namespace are stored in a repository. A forwarding rule is generated based on the namespace's stored configuration. When incoming IO having attributes similar to that type is received, implementation of the forwarding rule causes the incoming IO to be directed to the namespace. Attributes of a particular incoming IO are determined. As a result of the attributes satisfying a similarity threshold relative to the type, the forwarding rule is implemented such that the particular incoming IO is directed to the namespace.

Abstract: A method for holding an anomalous privileged operation, that includes receiving, by a privileged operations monitor executing on a data server, a privileged operation request from a client device, obtaining a plurality of metrics for the privileged operation request, calculating an anomaly score for the privileged operation request, based on the plurality of metrics, making a first determination, based on the anomaly score, that the privileged operation request is anomalous, and based on the first determination, placing a hold on the privileged operation request.

Abstract: A method for holding an anomalous privileged operation, that includes receiving, by a privileged operations monitor executing on a client device, a privileged operation request to copy data or modify data, obtaining a plurality of metrics for the privileged operation request, calculating an anomaly score for the privileged operation request, based on the plurality of metrics, making a first determination, based on the anomaly score, that the privileged operation request is anomalous, and based on the first determination, placing a hold on the privileged operation request.

Abstract: Non-volatile Random Access Memory (NVR) on a storage system may be dynamically converted between use as temporary memory in a memory context and use as persistent memory in a storage context. NVR (e.g., embodied as DIMM) may be utilized in a hybrid capacity, where some of the NVR is used as memory and some of the NVR is used as storage, and where NVR memory is converted to memory as needed, dynamically as I/O is being processed using the NVR. A host system may be directly connected to an internal switching fabric of the data storage system without an intervening component of the storage system (e.g., a director) controlling access of the host system to the internal fabric or to the memory. The host system may provision and use the NVR as storage by directly communicating with the NVR over the internal fabric, for example, using RDMA.

Abstract: Techniques described herein relate to methods for managing backup and restore operations. Such a method may include performing a vProxy health analysis to obtain vProxies assigned a healthy label; performing a confidence analysis to assign a health confidence score to each separate healthy label for each vProxy of the plurality of vProxies; filtering the plurality of vProxies to obtain a set of vProxies, each having a separate health confidence score over a confidence score threshold; calculating average throughput for each vProxy in the set of vProxies; assigning an optimal label to a vProxy of the set of vProxies based on the vProxy having an average throughput over a throughput threshold; and presenting a list of optimal vProxies comprising the vProxy to an entity configuring a backup job. The entity may select the vProxy for use in the backup job in response to being presented the optimal label of the vProxy.

Abstract: A method for processing backup policy generation requests includes receiving, by a production agent manager, a backup policy generation request, in response to receiving the backup policy generation request: identifying a backup to transfer to a backup storage, wherein the backup comprises a plurality of data blocks, performing a data storage device evaluation on a set of data storage devices to obtain a set of health scores, wherein each health score of the set of health scores corresponds to a data storage device of the set of data storage devices, assigning, based on the set of health scores, a production agent to a data storage device of the set of data storage devices to generate a backup policy, and initiating, based on the backup policy, a deployment of the production agent to the data storage device.

Abstract: A method and system for health rank based virtual machine restoration using a conformal framework. With respect to enterprise data protection, solutions need to address two primary responsibilities: at the onset of failure or disaster, restore any running applications, as well as any lost or damaged data; and minimize (if not eliminate) the future occurrence of such failures or disasters. In tackling the latter responsibility, the disclosed method and system leverage health-score assessments to ensure the restoration of virtual machines onto healthy infrastructure. The aforementioned health-score assessments employ clustering to identify, and a conformal framework to rank, healthy hosts onto which virtual machines may be restored.

Abstract: One example method includes identifying data attributes of a dataset that is protected by an encryption key, creating a causal model that indicates an impact that the data attributes have on each other and on a value of the dataset, determining, for each of the data attributes, and based on the causal model, an impact that each data attribute has on the value of the dataset, calculating, for each data attribute, a weight that indicates a magnitude of an impact that the data attribute has on the value of the dataset, calculating, using the weights, a criticality index for the dataset, and rotating, based on the criticality index, the encryption key so that the encryption key is replaced with a new encryption key.

Abstract: One example method includes collecting respective data for each datastore in a group of datastores, clustering the data, based on the clustering, applying one of two different mutually exclusive labels to each of the datastores, applying an uncertainty quantifier to determine, for each datastore, a confidence level that the label assigned to that datastore is the correct label, ranking the datastores according to their respective label and confidence level, and presenting, to a user, a list that includes at least a datastore with a highest confidence level.

Abstract: A self-guided troubleshooting guide generation method includes: receiving a first assistance request from the user; in response to receiving the first assistance request, scanning a first user interface (UI) through which the first assistance request is transmitted and extracting text from the first UI; parsing the extracted text and identifying an error message in the extracted text; comparing, after the identifying, the error message to data stored in an assistance request database; and generating, based on the comparing, solution information for resolving the error message and displaying the solution information on a display displaying the first UI.

Abstract: The described technology is generally directed towards a message broker resource monitoring service that obtains message broker resource parameter data of a resource, based on communications of a message broker. Based on the resource parameter data and historical data, message broker resource monitoring service obtains a predicted message delivery time value, which can be in association with confidence. If the predicted message delivery time value satisfies a resource deletion criterion, e.g., the predicted message delivery time value, with sufficient confidence, exceeds a threshold value, the message broker resource monitoring service triggers an action to delete the resource. To obtain the predicted value, a regression such as symmetric conformal quantile regression can be applied to the parameter data, e.g., to obtain a predicted message delivery time/latency value.

Abstract: One example method includes gathering respective performance data concerning each asset in a group of assets, clustering the performance data so as to define a first cluster and a second cluster, and data assets in the first cluster are assigned a HIGH RISK label and data assets in the second cluster are assigned a LOW RISK label, assigning a respective risk score to each of the assets, and the risk score includes a quantified risk level for the asset to which the risk score has been assigned, ranking the assets with the HIGH RISK label according to their respective risk scores, and backing up a ranked asset based on an IO volume associated with that ranked asset.

Abstract: One example method includes telemetry based state transition and prediction. Telemetry data is used to generate a transition matrix. The transition matrix is used to predict a state transition for a system or an application. A log level is predictively adjusted based on the transition matrix. The telemetry data is thus adaptively collected based on predicted transitions.

Abstract: Embodiments are described for an autonomously and dynamically allocating resources in a distributed network based on forecasted a-priori CPU resource utilization, rather than a manual throttle setting. A multivariate (CPU idle %, disk I/O, network and memory) rather than single variable approach for Probabilistic Weighted Fuzzy Time Series (PWFTS) is used for forecasting compute resources. The dynamic throttling is combined with an adaptive compute change rate detection and correction. A single spike detection and removal mechanism is used to prevent the application of too many frequent throttling changes. Such a method can be implemented for several use cases including, but not limited to: cloud data migration, replication to a storage server, system upgrades, bandwidth throttling in storage networks, and garbage collection.